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Among neural disorders related to movement, essential tremor has the highest prevalence, in fact, it is twenty times more common than Parkinson&rsquo, s disease. The drawing of the Archimedes&rsquo, spiral is the gold standard test to distinguish between both pathologies. The aim of this paper is to select non-linear biomarkers based on the analysis of digital drawings. It belongs to a larger cross study for early diagnosis of essential tremor that also includes genetic information. The proposed automatic analysis system consists in a hybrid solution: Machine Learning paradigms and automatic selection of features based on statistical tests using medical criteria. Moreover, the selected biomarkers comprise not only commonly used linear features (static and dynamic), but also other non-linear ones: Shannon entropy and Fractal Dimension. The results are hopeful, and the developed tool can easily be adapted to users, and taking into account social and economic points of view, it could be very helpful in real complex environments.

This paper is an extended version of one paper published in the 4th IEEE International Work Conference on Bioinspired Intelligence, Donostia, Spain, 9–12 June 2015 Biomedical systems produce biosignals that arise from interaction mechanisms. In a general form, those mechanisms occur across multiple scales, both spatial and temporal, and contain linear and non-linear information. In this framework, entropy measures are good candidates in order provide useful evidence about disorder in the system, lack of information in time-series and/or irregularity of the signals. The most common movement disorder is essential tremor (ET), which occurs 20 times more than Parkinson's disease. Interestingly, about 50%-70% of the cases of ET have a genetic origin. One of the most used standard tests for clinical diagnosis of ET is Archimedes' spiral drawing. This work focuses on the selection of non-linear biomarkers from such drawings and handwriting, and it is part of a wider cross study on the diagnosis of essential tremor, where our piece of research presents the selection of entropy features for early ET diagnosis. Classic entropy features are compared with features based on permutation entropy. Automatic analysis system settled on several Machine Learning paradigms is performed, while automatic features selection is implemented by means of ANOVA (analysis of variance) test. The obtained results for early detection are promising and appear applicable to real environments. This work has been partially supported by the University of the Basque Country under project ref. UPV/EHU-58/14, SAIOTEK program and others from the Basque Government, the Spanish Ministerio de Ciencia e Innovacion TEC2012-38630-C04-03, the University of Vic-Central University of Catalonia under the research grant R0904, INNPACTO program from the Spanish Government, and UPV/EHU Summer Courses Foundation.